docstr: Score `x` instances using the detector.
  
  Scores the data using the fitted svm model. The higher the score, the more anomalous the instance.
  
  Parameters
  ----------
  x
      Data to score. The shape of `x` should be `(n_instances, n_features)`.
  
  Returns
  -------
  Outlier scores. The shape of the scores is `(n_instances,)`. The higher the score, the more anomalous the         instance.
  
  Raises
  ------
  NotFittedError
      If called before detector has been fit.
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code: def score(self, x: np.ndarray) -> np.ndarray:
      """Score `x` instances using the detector.
  
          Scores the data using the fitted svm model. The higher the score, the more anomalous the instance.
  
          Parameters
          ----------
          x
              Data to score. The shape of `x` should be `(n_instances, n_features)`.
  
          Returns
          -------
          Outlier scores. The shape of the scores is `(n_instances,)`. The higher the score, the more anomalous the \
          instance.
  
          Raises
          ------
          NotFittedError
              If called before detector has been fit.
          """
      score = self.backend.score(self.backend._to_backend_dtype(x))
      return self.backend._to_frontend_dtype(score)
  
file_path: alibi-detect/alibi_detect/od/_svm.py
imports: from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
  import numpy
  from alibi_detect.base import BaseDetector, FitMixin, ThresholdMixin, outlier_prediction_dict
  from alibi_detect.exceptions import _catch_error
  from alibi_detect.od.pytorch import SgdSVMTorch, BgdSVMTorch
  from alibi_detect.utils._types import Literal
  from alibi_detect.utils.frameworks import BackendValidator
  from alibi_detect.version import __version__
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 3 for calling the SVM obj's underlying member method code with example usage: obj.score(x) and returns: self